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Elevations

Whether elevation is a separate topic or part of a discussion of geomorphic structures may not be of significance. The number of phenomena possible related to it make it a very important factor as people begin to comprehend the land and its potentials and limitations.

A special point of view, often over-looked and often at odds with some of the following statements:

Elevation itself is not important. It is not an ecological factor. It is something that we can observe but has no functional bearing or meaning. It is a locator, like latitude and longitude, specifying a point within the three dimensional thick zone around Earth. Temperatures change in relationship to it. Temperature is the key variable. Barometric pressure changes in relation to it. Pressure is the variable, not the correlated height above the sea. Factors are clustered into and easily analyzed under the elevation concept. How much water there is all of the time and how nutrients are supplied to plants in that heat-laden moisture are the key factors. They operate at different rates when at different elevations because there, temperature, pressure, and other basic factors differ. We have to include these factors in the maps, not their non-linear correlatives.

The adjacent map (planned) shows the 10 elevation classes of a county area. A similar map with color will be developed for each ownership. Color slides (not included here) will also been prepared.

Elevation is the height of the land surface above or below sea level. "Altitude" is sometimes used, but usually this denotes a location in the atmosphere such as achieved by aircraft. The maximum elevation in the US is Mt. McKinley, Alaska, 20, 320 feet. The minimum is in Death Valley, Inyo, California and is -282 feet. In the state and nearby states, the maximum elevations (US Geological Survey) are:

The maximum or highest point on the property is 3,721 feet above sea level.

The minimum is 2,040 2,040, and thus

The range is 1,6811,681 feet.

This range is also called the dissection index by some people. Some people think that the range strongly influences the potential biodiversity of the area, perhaps even more than the total area. The greater the range, the greater tend to be the richness or number of species in an area.

The elevation map does not replace a conventional contour map, but may augment it. Some users enjoy the additional perspective, and gain knowledge about high points and low points in an area. Elevation data are critical to many ecological, watershed, and other features ofthe land and it sets limits to potentials... but they open opportunities. The map suggests the average distribution of elevations and may assist in planning access or explaining inaccessibility. Only ten shades of gray are shown. There is within the computer an actual number representing the elevation above sea level of the middle (centroid) of every cell within the area.

Maps to be displayed for study within this elevation section of a Trevey report include:

  1. Three cross-sections or land profiles are to be shown.
  2. A bar-graph showing the rank order distribution of elevations of pixels in 10- foot intervals, low to high.
  3. A bar-graph showing the rank order of the cube root of the count of pixels in each l0-foot interval, low to high
  4. Bar graphs and tables of cells - elevation vs slopes, elevations vs aspects (not flat), elevation vs the slope/aspect union computed for site quality analyses.
  5. Reduced (lowered) surrounding area ( about 10% of the range), boundary; and 3-d elevations
  6. Contours within the boundary
  7. 3-d elevation with boundary, roads and streams draped.
  8. Elevation to the 1.5 or 2 power to accentuate the topography relative to 2 orientations.
  9. Elevation to other powers (experimental for visual effect) to represent the influence of elevation in other models. See additional suggestions and plans below.

A very rich source of ecological information...

General knowledge about elevations (compare to general observations about latitude and longitude)

  1. From elevation we can calculate slope, landform (slope position, slope, ridge, saddle, etc.) and several types of aspect (the direction that the slope faces; see later section). Elevation is a key factor in models estimating monthly temperatures in each map. Slopes based on elevations, are critical in solar energy models and thus affect stream temperature. In addition they are useful in computing solar energy and shadows on areas.
  2. "Proportionally less high area" may be a useful statement to make after a study of several areas.
  3. Proportions of an ownership within elevation classes may be useful information for comparing potential productivity.
  4. Uses for area reconnissance and orientation
  5. Delimiting on maps on a computer screen operational areas for rubber- tire tractor, cleat-tire tractor, and high-tech logging based on slope.
  6. Mapping monthly mean temperatures (in Virginia only, based on Anderson 1981)
  7. Estimating a relative forest index (productivity potential) based on slope and aspect derived from elevation data (Stage 1963, 1969, 1976)
  8. Calculating daylight hours based on topographic shadow (based in part on Lawrence 1976, Halverson and Smith 1979) for use in forest site evaluation and characteristics of recreational areas
  9. Calculating relative suitability of areas of deer based on energy cost of living in map-cell area
  10. Elevation influences barometric pressure and a relative barometric pressure map may be created, useful in computing a map of relative evaporation and evapotranspiration (see Klopfer 1998).
  11. Barometric pressure decreases xxx .. units for every 100 feet increase in elevation. '
  12. Evaporation decreases about % for every 100 feet increase in elevation. Solar radiation is reduced an average of ..xx .for every 100 feet of atmosphere through which it passes (close to Earth).
  13. Elevation also affects vapor pressure and thus insecticide effectiveness.
  14. Elevation is inversely correlated with temperatures. Thus reptile species richness and abundance are likely to be correlated with the warmer temperature areas.
  15. Nocturnal temperatures are high at higher elevations and within the canopy; cold air settles in valleys and lower elevations
  16. Elevation is a major variable in determining potential forest vegetation in western Virginia.
  17. The higher the elevation, the more northerly the conditions.
  18. The higher the elevations, the steeper the slopes.
  19. The greater the change in elevation (called by some "the range"), the greater the richness.
  20. There are effects of pressure on freezing, thus an elevation/temperature interaction on animal energetics. (When water freezes on the mountain top, does it freeze elsewhere? Does it thaw later?)
  21. The more coarse the resolution of the elevation data, the lesser become the slopes. Ecologically important places (e.g., rock faces, waterfalls, hiding places) become obscure. [Expert systems related work is likely to be able to locate and map such probable sites. RHG]
  22. Mueggler (1988) noted that occurrence of elevation limit to trees and shrubs generally increased with declining latitude. He discussed aspen (Populous tremuloides Mich.) and noted upper elevation limit seems primarily determined by the length of the growing season, the lower limit by evapotranspiration. In general site index tends to decrease as elevation increases but other factors can over-ride this influence.
  23. Elevation is a major variable in determining potential forest vegetation in western Virginia. It is a covariate with precipitation, a determinant of potential plant production. In upland pastures, for example, soil nutrient content tends to decrease the higher the elevation, and forage production tends to decrease. The climate (often including solar radiation differences, sunrise and sun set) at the higher elevation is different too. These factors are confounded and it is difficult to determine the contribution of each to the reduced growth. Learning the effect of elevation on plant growth may suggest cost effective means for overcomming limitations set by soil conditions and plant genotype. For example, high plant growth can be expected at high elevations after fertilization, but moisture available may determine the total production ... and cultural/herding practices will determine its utilization. Hunter and Grant (1971) found that the climatic variable of greatest effect was temperature, a function of elevation, and that the closer the temperature to the optimum for plants, the less the effect of elevation.
  24. The range of many species is set by the upper elevation limits - some complex of strongly temperature-related factors. Shine et al.(2002) observed that global temperature change may thus affect the upper elevation limit and thus, significantly, the species range and suitable area available. They noted the major influence possible on egg laying on reptiles. Global temperature change is not needed since habitat alterations (e.g., roads, clearings, powerlines) can increase temperatures and thus species can "penetrate higher into montane areas than otherwise possible."

We are aware that elevations vary greatly and that using a single elevation to represent the average for a lO x 10 meter area may not be sufficiently accurate for some purposes. The issues of map scale and cell-size are many and varied, but the first that must be faced is the cost of data collection, verification, storage, and retrieval. Analyzing average-sized areas requires hundreds of thousands of map cell (even for small areas). The collection costs are high, the accuracy of recorders (eye-strain, care, etc.) always a concern, and storage space for data (including backups) a continuing problem. Retrieval costs can be high.

Accuracy

No apology is made for the current elevation data base. It is too gross for some work, too precise for other work. It has been excellent in wildland work. See paper by Hesselton on elevation accuracy.Other sampling and data collection is underway. The present data cannot be made more precise but it can be aggregated.Sampling of elevation across a map may lead to inaccuracies. Grid points over a map are a systematic sample of the elevations of a surface. Acceptable, confidence levels and risk of error must be addressed in the context of natural variance, bias, and the conventional topics of rational statistical sampling.

At the practical level, it must be realized that many of the map sources, though excellent, are made by humans from aerial photo interpretations and field surveys. In some areas, radar-based instrument and satellite images are used. The accuracy increases as more funds become available and techiques improve. Experience will suggest there are dimensions of art as well as science in using any procedure for map making. Maps shrink and swell; overlays shrink and swell; the printing process changes some map scales (thus the actual location of a point on the ground represented on paper). Of course elevations themselves change in some areas from roads, land subsidence, and development. Gullies, upthrown tree roots, large rocks, mud or rock slides, and general surface and rill erosion can readily result in differences of a few inches to over 10 feet. Such differences are

We use DEMs or digital elevation models, data purchased from the U.S. Geologic Service, Washington, DC. DEMs are point elevations arranged in a grid pattern. Slopes, for example, are determined from them by comparing changes in the x and y axes in neighboring grid cells (e.g., Ritter 1987). Within 30 m DEMs, one point, the average elevation, is represented.

The elevation data are critical to many ecological, watershed, and other features of the area.

From elevation we can calculate average slope, land form (e.g., slope, ridge, saddle, etc.), and aspects type 1 and 2 (the directions that slope faces).

Elevation is essential in wind models.

Species richness (all categories of fauna;(Cascades; Harris et. al. 1982:382) is inversely related to elevation.

It is a key factor in models estimating monthly temperatures in each cell.

Slopes, based on elevations, are critical in solar energy models and thus affect stream temperature. In addition, they are useful in computing solar energy and shadows on areas.

Elevation influences barometric pressure. Therefore, elevation is important in evaporation models, which are components of (or are planned for) other analyses of the area.

Pressure gradients for the area may be useful and will be better correlated with some ecological factors than simple estimates of elevation. Gross temperature estimates for an area can be corrected, making the more correct than un-revised observations.

Since there are differences in atmospheric density and it is 1.23 grams per liter at sea level, then a logarithmic relationship can be developed to compute estimated density as a function of elevation. The map of probable atmospheric density may correlate well with other ecological variables ... become a factor in multiple regressions that help improve the R2 values for the equation, expressing the variance that has been accommodated.

The weight of air mass is >14,000 long tons.

It may be interesting to compare the weight on a person or deer at the top and bottom of a mountain.

The pressure is 1.02 times greater in the summer ; 0.97 in winter. (p.76; Wang)

Consider map of millibars of pressure as a percentage of sea level pressure for the growing season.

Generally a value of 0.0055 degrees F loss can be estimated for each foot of rise in elevation (dry-lapse rate). The wet-lapse rate is 0.0032 degrees per foot increase.

Humboldt observed that 1 degree latitude difference was about a difference of 1 degree Fahrenheit. 1 degree F is equivalent to about 365 ft altitude, 300 feet in the mountains.

One minute of arc at the Earth equator is 1.85325 km or 1.15155 miles.

In the mountains of the semi-arid West, Alter (1941) observed a change in 2500 feet equivalent to 500 miles in latitude. This is equivalent to 434.2 minutes in latitude of 7.2 degrees. This is 0.1735 (434.2/2500) minutes of latutude change per foot of change in elevation. This is a change of 0.0028946 degree per foot of elevation. )

Areas change phenologically as a consequence of the time of the year and its latitude, longitude, and elevation. All of the reasons are not known but they are surely the interplay of tenperature, solar radiation, evapotranspiration as influenced by barometric pressure, and other factors.

Hopkins observed equivalents of 1 degree of latitude with 400 feet elevation and 5 degrees of longitude. This is 0.15 (60/400) change in latitude in minutes per foot of elevation (0.0025 degrees/foot) and 0.75 (300/400) minutes of change in longitude (0.0125 degrees / foot) (in general in North America , eastward). McCombs in his Masters degree thesis made a GIS map of this relationship in Virginia, using the extreme SE corner of the state as the point for relative comparisons. Klopfer has done further work on the topic. Hopkins work had an error, but the relationship seems to hold and refinements will probably pay off.

Topographic shadow (suggested in blue (B) at the right) is a function of many things and even naming and deciding emphasis among them is difficult. Shadows cast by elevations on ot the distant slope (A) can influence temperature and moisture available there. The secondary effect of elevation - the shadows cast during the growing season and on modified temperatures and evapotranspiration on these shaded areas (and the associated GIS maps) - are probably much more significant and better correlated with plant and animal behavior/ functions than elevation itself. Every hunter knows the significant effects of topographic shadow. Similar studies and maps need to be created for lunar forces.

Soil and Elevation Relations

Soil trends on an western US elevational gradient, high to low, as follows:

These same relations need to be studied and generalized for the Eastern areas.

We are well aware that elevations vary greatly and that using one elevation to represent the average for a square area (a pixel or picture element) ) may nof be sufficiently accurate. The issues are many and varied, but the first that must be faced in the past 30 years (as well as now, 2002) is the cost of data collection, storage, and retrieval. Analyzing one average-sized area requires dealing with about 2000 cells. To change from 27-acre to 3-acre cells requires dealing with 18,000 cells. The collection costs are higher (eye-strain, etc.), accuracy of recorders more questionable, computer storage and retrieval costs higher. A simple decision to cut the cell size from 27 to 3 acres may increase costs by about 15 times.

Sampling is another issue. Grid points are a systematic sample of the elevations of a surface. Acceptable confidence levels and risk of error must be addressed in the context of natural variance, bias, and the conventional topics of rational statistical sampling.

At the practical level, it must be realized that the map sources, though excellent, are made by humans from aerial photo interpretations and field surveys. Recently, other techniques (e.g., radar) have been used in some areas. Experience with either will suggest there are dimensions of art as well as science involved. Maps shrink and swell, overlays shrink and swell, the printing process changes some map scales (thus the actual location of a point on the ground represented on paper). Of course, elevations themselves change in some areas from roads, subsidence, and development. Gullies, up-thrown tree roots, large rocks, mud or rock slides, and general surface and rill erosion can readily result in differences of a few inches to over 10 feet. Such differences are (a) typically irrelevant, (b) variable, unique, and/or unpredictable, (c) dynamic (tommorrow's events), and (d) can be, and are fairly readily, "smoothed out" or made more extreme (e.g., road cuts) by any activity in the area (i.e., made irrelevant).

As resources become available and as the needs for precision become more evident, it is highly likely that a more refined data base or set of equations can be developed to represent the surface of areas within Virginia. Techniques called kriging have been developed. It can be used to estimate values on the surface of a map (like elevation) given known values from samples at surrounding points. GPS will increase accuracies.

We have explored use of the computer tapes containing elevations of Virginia prepared by the U.S. Army Map Service and found them very gross and with inaccuracies that exceed our needs. These tapes were taken from 1:250,000 contour maps. New tapes are reported as being developed and may be very useful in the future.

Plans are for the typical Trevey Report to include:

  1. Relative elevation with all observations re-scaled to the lowest point on the ownership.
  2. Elevational emphasis map, re-scaled with elevation x 3.1416 mapped
  3. Range of elevation within the county, the ownership, the state, max and min and the difference (because of differences in definition of "range"
  4. Mean elevation and standard deviation, ownership, county, and state comparison
    Number of Oregon amphibians, reptiles, and mammal species (108 total) whose ranges transcend noted elevation points (from Harris, Maser, and McKee 1982).
    Elevation Class Number of
    Mammals
    500
    1000
    2000
    3000
    4000
    5000
    6000
    7000
    8000    		 95
    95
    90
    84
    68
    60
    40
    32
    17

    The mammals of the area can probably be ranked ordinally as a function of the elevations at which they are found.

  5. Graph of elevation frequency distributions
  6. A line graph of the area of each 20-foot contour interval within the management unit (one vertical dimemsion of a graph)
  7. A block diagram of the proportion (percentage) of the total area within each 20-foot interval of elevation (another vertical dimemsion, same graph; horizontal dimension 20-3200 "The elevation units"
  8. Elevation is only a crude expression of a probable moisture relationship because of other topographic effects (of slope and aspect). Nevertheless, moisture is the principle variable affecting vegetative cover. Whittaker and Niering found NPP= 3000 / (1 + e1,315 - 0.119MAP)

    where MAP is the mean annual precipitation in mm/year (the max NPP is 1500, the minimum is 0.) This equation needs to be mapped, cell by cell.

    For most climax temperate forests and woodlands, forest above-ground net productivity is 600- 1200 g/m2/yr

    Elevations in a watershed along with those within the region or management area can suggest similarities or factors affecting observed differences. (A simple bar showing the relations such as shown at the left can be useful.)

  9. Cumulative graph of area, lower elevations to higher (or simple illustrations of limits within the region sush as shown here
  10. Slope as a function of elevations in all pixels
  11. Graph of elevations within aspects (proportions)
  12. Elevation in relation to soil type
  13. Elevation and evapotranspiration
  14. Elevation and precipitation
  15. Water quality and elevation
  16. Stream velocities and elevation
  17. Stream order and elevation
  18. Growing season and elevation
  19. Soil moisture and elevation
  20. Frost-free periods as function of latitude, longitude, and elevation
  21. Work with plant pathologists to map diseases and periods of likely occurrence as a function of elevation
  22. Evaporation from waterholes as a function of elevation
  23. Weight the grasses in terms of their suitability (best grown) at elevations and develop a grass suitability map (See Anderson)
  24. Relate sap flow (maples) to elevation
  25. Create a recreational-camping suitability map (temperatures and clothing/sleeping gear related to elevation)
  26. Determine whether temperature and day-length effects are cumulative on grasses growth and productivity
  27. Revise Hopkins' "bioclimatic" law for elevation equivalents to latitude and longitude changes (see past work of McCombs and Klopfer)
  28. Number of species (likely richness by faunal groups) in each county ranked by central or average elevation

    Prepare discussions of the effects of elevation on the species area curve related to islands. The relation may be developed :

    S = cSz elevation, edge-length of area

    There has to be a north-aspect function equivalent to an increase in elevation. This needs to be modeled.

    It is claimed that plants trade off seed vs vegetative reproduction in their reproductive strategies. This may be included. How do plant types (and energy for animals) vary with elevation?

    Tables need to be created showing 20 elevation classes (highest to lowest elevation in the ownership split into 20 equal classes) with number of map cells in each class and the proportions of the total in each class. A graph of the distribution may provide insights (frequency plotted against elevation classes). Slope class frequency may be directly related.

    There remains considerable work on even the most simple of observations and then its relationships such as:

    1. To determine sources and costs of superior local elevation data
    2. To study precision, accuracy, and confidence as related to elevation descriptors
    3. To summarize ecological factors that are strongly a function of elevation or its fundamental forces
    4. To study correlatives of elevation, i.e., knowing elevation give what mastery of ecosystem function
    5. To produce GIS maps of elevation, transformed elevation, and factors that are primarily a function of elevation. For example, slope and aspect can be derived from elevation data, and these have been used as major determinants of forest site index (Stage 1963, 1969, 1976). Halvorson and Smith provide access to daylight hours related to topographic shadow.
    6. To discuss "Elevation: a derived or apparent ecosystem variable" (including relative elevation vs sea-level standards, the significance of barometric pressure as the inverse effect of height above sea-level, and the possibility of a cell-specific combined index variable, a "latitude/longitude/relative-elevation effect" that is almost unique for every cell. We have to find the upper elevation above which chlorophyll-bearing plants cannot live. This may define the relevant maximum elevation of significance to observable plant and animal systems (not some arbitrary upper limit.)

      A graph of simple elevation, latitude, longitude relations within a large area may be useful.

    7. To develop a rapid means to describe the land surface (elevational differences) of an ownership, one easily recognized by someone not accustomed to daily use of maps or GIS output. (This is likely to include a 3-dimensional view with elevation of each map cell over emphasized by some factor (say 10 times ... and to be discovered from interviews) more than latitude and longitude. This will probably include a first appearance for the owner "as if looking upstream" or some similar convention.
    8. Relative relief involves perimeter and difference in two elevation measurements
      Relief is the difference between the maximum and minimum elevations. The elevation ratio is the maximum / minimum where "flat" is zero. The relative relief is the perimeter / relief but some define it as area / relief.







    *Early work on programs related to elevation was done by Adrienne Nemura and editorial work was done by Thresa Vinardi and Susan Hamilton (1983).

    References

    Anderson, D. R. 1981. A climatological information system for natural resource management: temperature. M.S. Thesis, VPI and SU, Blacksburg, Virginia. viii + 220 pp.

    Bergen, J.D. 1971. Topographic effects apparent in nocturnal temperature profiles in a conifer canopy. Agric. Meterology 9:39-50

    Cooper, C.F. 1986. Carbon dioxide enhancement of tree growth at high elevations. Science vol 231, 21 Feb p. 859

    Elliott, K.J. and D. Hewitt. 1997. Forest species diversity in upper elevation hardwood forests in the Southern Appalachian Mountains. Castanea 62(1): 32-42

    Fies, M. L. Predicting forest cover types in Southwestern Virginia using topographic information. M.S. Thesis, VPIandSU, Blacksburg, Virginia. xii + 234 p.

    Grender, G. C. 1976. TOPO III. A FORTRAN program for terrain analysis. Computers and Geosciences 2: 195-209.

    Halverson, H. G. and J. L. Smith. 1979. Solar radiation as a forest management tool: a primer of principles and application. Gen. Tech. Report PSW-33, Pacific Southwest Forest and Range Exp. Station, Berkeley, CA. 13 pp.

    Harris, L.D., C. Maser, and A McKee. 1982. Patterns of old growth harvest and implications for Cascades wildlife. Trans. N. Amer. Wildlife and Nat. Res, Conf, 47: 374-392.

    Hamill, J. F. 1976. A computer-based methodology for estimating potential wildlife productivity for large areas. M.S. Thesis, VPIandSU, Blacksburg, Virginia. x + 94 pp.

    Hoar, A. R. 1980. A method for mapping the probable ranges of endangered mammals in Virginia. Unpub. M.5. Thesis, VPIandSU, Blacksburg, Virginia.

    Hunter, R.F. and S. A. Grant. 1971. The effect of altitude on grass growth in East Scotland. J. Appl. Ecol 8(1):1-19

    Jones, A. B. 1976. Power: a computer information system for land use decisions. M.S. Thesis. VPI and SU. , Blacksburg, VA. 91 pp.

    Lawrence, G. E. 1976. A computer-based insolation mapping algorithm for large areas. Unpublished M.S. Thesis, VPIand SU, Blacksburg, Virginia. viii + 129 pp.

    Lesure, F. G. 1957. Geology of the Clifton Forge iron district, Virginia. Bulletin of the Virginia Polytechnic Institute, Engineering Experiment Station Series No. 118. 130 pp + plates.

    McGuire, 0. S. 1970. Geology of the Eagle Rock, Strom, Oriskany and Salisbury Quadrangles, Virginia. Virginia Division of Mineral Resources, Report of Investigations, 24, 39pp + plates

    Shine, R., E.G. Barrott, and M.J. Elphick. 2002. Some like it hot: effects of forest clearing on nest temperatures of mantane reptiles. Ecology 83(10):2808-2815

    Stage, A. R. 1963. A mathematical approach to polymorphic site index curves for grand fir. For. Sci. 9: 167-180.

    Stage, A. R.1969. Computing procedure for grand fir site evaluation and productivity estimation.USDA For. Service Res. lab INT-98, Ogden, Utah.6 p.

    Stage, A. R.1976. An expression for the effect of aspect, slope, and habitat type on tree growth. For. Sci. 22:457-460.

    Whittaker, R.H. and W.A. Niering. 1975. Vegetation of the Santa Catalina Mountains, Arizona. V. Biomass, production, and diversity along the elevation gradient. Ecology 56: 771-790.

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